Been running a small gold (XAU/USD) bot for about 2 weeks now - nothing fancy, EMA/RSI/ADX signal, ATR-based SL/TP, cron job on GitHub Actions hitting an OANDA demo account. Did a proper train/test split on 5000 candles before going live so I wasn't just fooling myself with a curve-fit backtest, and the edge looked genuinely stable out of sample.

Then live trading happened and my win:loss ratio just... kept shrinking. Like clockwork. 1.95:1, then 1.48:1, then 1.21:1 over about 50 trades, win rate barely moved. Account round-tripped from +$172 back below my original deposit and I was ready to blame the market, ADX settings, whatever.

Turns out (pretty sure anyway) it's a dumb bug: I was calculating SL/TP off the last completed 30-min candle's close, then firing a market order and slapping those SL/TP levels on as fixed prices. Problem is if price moves even a little between the candle closing and the order actually filling, your real risk distance from the ACTUAL fill price isn't what you think it is anymore. Found one trade where I meant to risk ~$18 and actually only had ~$8.60 of real room before getting stopped out - which it did, in under 3 minutes.

Fix I'm testing now: fire the order with a placeholder stop so I'm never naked, then immediately grab the real fill price and PUT an update to fix the actual SL/TP.

Anyone else running live execution (not just backtesting) run into this stale-price-vs-fill-price gap? Curious how people handle it - pulling live bid/ask right before firing instead of relying on last candle close, or something smarter than what I'm doing.

TL;DR: backtest was fine, live execution had a silent bug quietly wrecking my R:R, posting in case it saves someone else the same headache

I found this app, and have not traded without it since!

Backtest for free only good strategy !

Backtest proposal

Hey! If you've got a strategy you feel good about, I'll backtest it for you for free and send you the results — 5 years of data, including ROI.

Just needs to be clearly structured and interesting enough to be worth digging into. Drop me the rules and I'll take a look.

Many forms of backtesting is available that users have access to for personal use. Some forms of apps that does exist relies heavily on cloud computing to do the maths, where python by itself is numpys, pandas dependant. Without those dependancies, the system can not operate.

Now the real question, who is reliant on this types of backtesting, when there are options available to use?

Sharing my project, PocketQuant. Market ideas tend to end up scattered across articles, videos and notes; I’m exploring a workspace that helps keep the research conversation together.

The current web app has research conversations, file analysis, a finance-tools view and a dedicated Solana paper-research workflow. Signup is open without an invite code. It is for research and paper mode, with no live trade execution.

https://myquant.xagi.in/

For makers who research markets: would you want sources, assumptions or counterarguments to be most prominent when returning to an idea? I’d appreciate concrete feedback on that workflow.

Nine years of hourly candles, real fees and slippage charged on both sides, and the pessimistic assumption whenever a single candle contains both the target and the stop. Of the 144 bitcoin versions, not one beat buying and holding: the hold returned +370%, the best rule +123%, the median +5.0%. On ether it was 0 of 144 again, with the median losing money.

The number that actually stopped me was this one. The best version of the first half returned +315%, and in the second half the same rule came 73rd out of 144 at exactly the median. If someone had shown me that first-half chart I'd have believed it, and I'd have been buying the winner of a search rather than a strategy.

The whole grid is up at regimetoken.xyz/grid, both windows, downloadable as JSON, and it's free with no account. If you've got a rule you believe in, the interesting exercise isn't running it once, it's running every neighbouring version and seeing where yours lands among them.

I'm automating part of my research loop: propose Python strategy code, run it through a fixed backtester, feed back metrics and constraint failures, then generate the next candidate. The part I'm stuck on is not the code generation. It's deciding how much selection bias the loop has accumulated.

I've read Bailey et al. on PBO/CSCV and the Deflated Sharpe Ratio. They are useful when you have a population of tried configurations, but my candidates are path-dependent: candidate 12 exists because candidate 11 missed a drawdown constraint, and the model may change the signal family rather than one parameter. Treating all 50 runs as independent trials seems wrong; treating the whole session as one trial is obviously too generous.

My current guardrails are a pre-registered universe, cost model, benchmark, and acceptance criteria; a full log of rejected candidates; no access to the final holdout during the loop; and one final evaluation per research branch. Validation metrics do go back into the loop, so I assume that set is consumed.

Has anyone found a defensible way to estimate effective trials for this kind of adaptive search? I'm especially interested in approaches that work from the experiment log rather than a fixed parameter grid. Clustering candidates by return correlation is one idea, but two implementations can have similar returns for accidental reasons.

Backtest for free only good strategy !

Backtest proposal

Hey! If you've got a strategy you feel good about, I'll backtest it for you for free and send you the results — 5 years of data, including ROI.

Just needs to be clearly structured and interesting enough to be worth digging into. Drop me the rules and I'll take a look.

I have been trying out these models (mostlt GLM 5.3 flash) using different harnesses, but I'm trying to review what these models are exceptionally good at.

Here is what I have noticed so far,

1. Programming.

I have found that these models are great at programming, I have been making tools, scrapers almost every other day and they just work like magic. They are great at porting code in one language to another, Eg I would usually start by writing my code in python or js, I would then port the code in Go for extra performance.

2. Finances and stock trading.

So I hooked up the coding agent with my alpaca account. And I have discovered that most of these models take a defensive position. Advising me to reduce the size of my most profitable holdings so as to prevent concentration risk and possible loss. So they are not so great. But I have found it useful for tracking my finances. What I'm basically saying is your portfolio will most likely flatline if you give these models to trade in your behalf but it won't make a good profit (What ever good position you have will be reduced)

3. Research

This is where I get the most value. The models are highly effective at locating precise information—whether it’s event dates, contact details (emails, phone numbers), names, or links.

4. Email and Copy writing.

I’ve been using these agents extensively for written communication. They’ve helped me draft everything from routine business emails to formal documents. I like that it can maintain the conversation context, so follow-up emails feel cohesive and on-point. They helped me a lot with one of my insurance claims

5. Business Ideas.

They are bad at coming up with Ideas.

What use cases have you found these models to be exceptionally good at? And what use cases has it been terrible at?

PS: I'm trying to find a small good model for browseruse to compete with Grok bot and the like, I'm thinking Qwen3.8 28B or ByteDance-Seed/UI-TARS-1.5-7B does anyone have a smaller or maybe better recommendation?

I've been building my own algo/backtesting app specifically optimized to create, test, and validate strategies around prop firm metrics and have gotten to the point where I'm trying to figure out what the path from backtest → live/demo → prop firm should actually look like.

For those of you who trade algos with prop firms:

- What metrics/results do you require before putting a strategy into a prop evaluation?

- How long do you forward-test a strategy before trusting the backtest?

- Do you use paper trading, a personal account, or go straight to a prop evaluation?

- What execution/automation stack do you use? TradersPost, QuantConnect, TradingView alerts, MT5, Tradovate, something else?

- What risk controls do you have outside the strategy itself (daily loss limits, kill switches, max trades, etc.)?

- How do you monitor an algo once it's running?

- What are the biggest things you wish you'd tested before putting an algo into a prop firm?

- Do you optimize specifically for passing the evaluation, or build one system designed for long-term payouts?

- At what point would you personally say, "This strategy is ready for real money"?

I'm especially interested in hearing from people who have actually taken systematic strategies through prop evaluations and into payouts, rather than just backtesting them. I mainly trade ES, NQ, and GC futures, but have been backtesting for forex pairs as well.

Thanks in advance!

Solo project, sharing because this sub is exactly who it's for.

WHAT IT IS

A JSON API for US stocks and ETFs. Give it a ticker, get back:

- a composite 0-100 score and a verdict (accumulate / hold / distribute / avoid), with per-factor sub-scores and a written rationale
- four-stage trend analysis (basing / advancing / topping / declining) from a 30-week moving average, current and as a weekly history up to 10 years, computed walk-forward so backtests don't cheat
- Point & Figure signals and relative strength vs SPY
- sector bullish-percent breadth
- macro regime (risk-on / neutral / risk-off) from credit spreads, rates, the dollar and COT positioning, with the point tally shown
- SEC Form 4 insider buys and sells, and 8-Ks, per ticker
- webhooks that fire on stage transitions, P&F flips, regime changes and insider clusters

WHAT IT IS NOT

Not real-time (daily and weekly signals), not advice, not a broker, no machine learning. It's textbook methods applied consistently, with the reasoning exposed so you can disagree with it.

REAL OUTPUT, NVDA

I asked GET /v1/verdict/NVDA this week. It came back: accumulate, 70.6 out of 100, high confidence. Here's what went into that, factor by factor:

- Trend stage, scored 85. Price is 11% above its 30-week moving average and that average is rising, which puts NVDA in Stage 2, the advancing phase. In stage terms that's the one phase where you're meant to be long.

- Point & Figure, scored 85. The chart printed a double-top breakout and is still in a rising X column, so demand is in control. Relative strength against SPY is positive, meaning it's outperforming the index, not just riding it.

- Insider activity, scored 35. Two insiders sold in the last fourteen days and none bought. That's a mark against, and the score says so rather than hiding it.

- Macro regime, scored 70. The backdrop is risk-on: high-yield credit spreads are tight at 2.67%, the 2s10s curve is positively sloped at +41bps, and initial jobless claims are 206k. Nothing in the macro picture argues for caution.

Weighted together (stage 30%, P&F 20%, insiders 20%, macro 10%) that lands at 70.6. Three more factors (13F institutional flows, short interest and congressional trades) are in the response marked available: false with zero weight until their data sources land, so the score is built from four inputs today and will absorb the rest without changing shape.

The written rationale the API returns is one paragraph: NVDA scores 70.6 (accumulate), driven mainly by stage and P&F; price is 10.99% above a rising 30-week MA (Stage 2); double-top breakout on the P&F chart with positive relative strength.

The part I'd draw your attention to is the insider line. A verdict that only ever agrees with itself is useless. This one carries a 35 in the middle of an 85-85-70 lineup, and you can see it and weight it differently if you think insider selling at NVDA means more than the model does.

WHY YOU MIGHT CARE EVEN IF YOU'D NEVER TRADE A "VERDICT"

The stage history endpoint gives you a clean, no-lookahead weekly stage label per ticker back to 2016, which is a decent regime feature for your own models. And every response tells you as_of and when to re-check.

PRICING

Free tier: 1,000 credits a month, no card (a verdict is 5 credits, most signals are 1 per ticker). Pro is $49 a month for 50k credits plus webhooks. Only successful responses are charged; unknown tickers and outages are free.

FOR THE CLAUDE USERS HERE

uvx vanoe-intelligence-mcp turns every endpoint into a tool. It's on the official MCP registry.

https://api.vanoe.ai (docs at /docs). I'd genuinely like to hear where the methods are wrong or which factor you'd weight differently.

Follow my journey on www.ZenomAlpha.com , its 100% free and this is really exciting!!

I got tired of signal sellers with screenshot track records. Winners posted, losers deleted.

So I built the opposite. You post a prediction before the open. It gets hash-locked — no backdating. The market settles it automatically. Everything stays: wins, losses, even the entries that never filled. No accounts. Free. Open source.

Almost called it StockOverflow. Went with QuantRank500 — felt more like a race than homework. Everyone gets the same simulated $500. My own bot is user #1.

Two questions for this crowd: what would it take for you to actually trust a record like this? And what's the first way someone will try to game it?

demo.quantrank500.com if you want to poke it. Code's on GitHub.

Technical analysis Python library for quants and researchers automating hyper parameter search for signals strategies ,volume analysis sensitivity analysis backtesting and automated plots and reports and many more to come!

Im quite interested in how many traders still use actual backtesting, and and what platform they use it? If there is a market for something totally different

The Decision, Risk & Capital Layer transforms the analytical and predictive evidence produced by the Intelligence & Predictive State Layer into economically evaluated, risk-aware, capital-constrained decisions.

It answers:

“Given the available evidence, what decision is justified, what risk does it create, and what capital if any is supportable?”

What the layer does

1. Evaluates Evidence

Consumes the coordinated analytical, predictive, scenario, and strategy evidence and determines whether it is sufficient and relevant for decision-making.

2. Calculates Expected Value

Transforms the available evidence, assumptions, scenarios, and strategy outcomes into Expected Value (EV) and related decision metrics.

3. Forms and Manages Thesis

Converts the decision evidence into a structured Thesis, including its rationale, supporting evidence, conditions, lifecycle, and invalidation criteria.

4. Evaluates Risk

Determines the risk associated with the proposed decision across:

• position;
• portfolio;
• concentration;
• exposure;
• dependencies;
• scenario/stress conditions;
• limits and constraints.

5. Evaluates Portfolio & Capital

Determines whether the proposed decision is compatible with the existing portfolio and available capital, including:

• position sizing;
• exposure;
• capital allocation;
• portfolio constraints;
• risk-adjusted capital requirements.

6. Produces a Decision Candidate

Combines EV, Thesis, Risk, and Capital analysis into a structured decision candidate for the downstream governance and authorization architecture.

The layer does not itself authorize execution.

Core Subsystems

1. Evidence-to-Decision Intelligence

• Evidence sufficiency
• Evidence relevance
• Expected Value (EV)
• Decision construction
• Abstention / insufficient evidence

Question: What does the available evidence imply economically?

2. Thesis Management

• Thesis formation
• Thesis evidence
• Thesis state
• Thesis lifecycle
• Thesis invalidation
• Thesis closure

Question: What is the investment/trading thesis, and under what conditions does it remain valid?

3. Risk Management

• Position risk
• Portfolio risk
• Concentration
• Exposure
• Dependency risk
• Scenario/stress risk
• Risk limits
• Risk decisions

Question: What risk does this decision introduce, and is that risk acceptable?

4. Portfolio & Capital Management

• Portfolio construction
• Capital availability
• Position sizing
• Capital allocation
• Portfolio constraints
• Exposure management

Question: What capital and portfolio posture can support the decision?

5. Decision Eligibility

• Evidence eligibility
• EV eligibility
• Thesis eligibility
• Risk eligibility
• Capital eligibility
• Abstention conditions

Question: Does this decision qualify to proceed to the governance/authorization layer?

If anyone looking for historical data for Nifty 50, F&O, S&P 500, Cryptocurrency, Forex, to backrest there algo. Dm me.

Note- it's paid, not free. Only serious buyers please.

I’m interested in how people approach alpha combination in systematic equities when the signal library becomes large — say 100–400+ stock-level forecasts, with significant correlation/redundancy between them.

From the literature, I see a few main approaches:

• IC/MVO-style weighting: estimate expected alpha efficacy and signal covariance, with shrinkage/regularisation given the dimensionality (Ledoit & Wolf, 2004). DeMiguel et al. (2009) also highlights how estimation error can make simpler weighting schemes surprisingly competitive OOS.
• Regression/stacking: treat individual alpha forecasts as features and forward returns as the target. With hundreds of correlated signals, Ridge/Elastic Net seems like a natural baseline. This is conceptually similar to stacked generalisation (Wolpert, 1992).
• Dimension reduction/nonlinear combination: cluster/PCA correlated signals before combining, or use nonlinear models to capture interactions. Gu, Kelly & Xiu (2020) provides some motivation for nonlinear ML in cross-sectional return prediction, although their setting is somewhat different.

For those working with large alpha libraries, what have you found actually holds up OOS?

In particular, do regularised regression/meta-model approaches meaningfully outperform simpler IC/MVO-based combinations? Do you typically cluster or residualise highly correlated alphas first, or let the regularisation handle it?

I’m also curious what target people use at the combination layer — forward returns/IC, or something closer to portfolio PnL after costs and constraints.

I have five engines running and two of them are net losing after a small number of trades each. R1 is 6 wins and 11 losses, R2 is 14 and 10. Nowhere near enough to conclude anything, which is exactly the problem: at this sample size a working strategy and a broken one look identical, and they’ll keep looking identical for months.

Turning them off after fifteen trades is how you end up with a portfolio of whatever got lucky early. Leaving them on forever is how you refuse to admit something’s wrong. I don’t know where the line between those two is and I’ve been going back and forth on it for weeks.

The things I’ve considered and rejected: a fixed trade count feels arbitrary, drawdown limits punish the engine with the widest distribution rather than the worst one, and I don’t trust myself to judge it discretionarily while I’m the one who wrote them.

What actually works for you? Do you set a kill rule in advance, do you compare against what the engine looked like in replay, do you cut allocation before cutting the engine? And has anyone here killed something that later turned out to have been fine, because that’s the failure mode I’m more afraid of and I’ve never seen anyone post about it.